Integrated machine learning reveals the role of tryptophan metabolism in clear cell renal cell carcinoma and its association with patient prognosis

作者：Fan Li, Haiyi Hu, Liyang Li, Lifeng Ding, Zeyi Lu, Xudong Mao, Ruyue Wang, Wenqin Luo, Yudong Lin, Yang Li, Xianjiong Chen, Ziwei Zhu, Yi Lu, Chenhao Zhou, Mingchao Wang, Liqun Xia, Gonghui Li, Lei Gao · 发表于：Biology Direct · 年份：2024 · DOI：10.1186/s13062-024-00576-w · 被引用次数：10 · 研究领域：Ferroptosis and cancer prognosis、Cancer Immunotherapy and Biomarkers、Tryptophan and brain disorders

BACKGROUND: Precision oncology's implementation in clinical practice faces significant constraints due to the inadequacies in tools for detailed patient stratification and personalized treatment methodologies. Dysregulated tryptophan metabolism has emerged as a crucial factor in tumor progression, encompassing immune suppression, proliferation, metastasis, and metabolic reprogramming. However, its precise role in clear cell renal cell carcinoma (ccRCC) remains unclear, and predictive models or signatures based on tryptophan metabolism are conspicuously lacking. METHODS: The influence of tryptophan metabolism on tumor cells was explored using single-cell RNA sequencing data. Genes involved in tryptophan metabolism were identified across both single-cell and bulk-cell dimensions through weighted gene co-expression network analysis (WGCNA) and its single-cell data variant (hdWGCNA). Subsequently, a tryptophan metabolism-related signature was developed using an integrated machine-learning approach. This signature was then examined in multi-omics data to assess its associations with patient clinical features, prognosis, cancer malignancy-related pathways, immune microenvironment, genomic characteristics, and responses to immunotherapy and targeted therapy. Finally, the genes within the signature were validated through experiments including qRT-PCR, Western blot, CCK8 assay, and transwell assay. RESULTS: Dysregulated tryptophan metabolism was identified as a potential driver of the...